PUBLICATIONS
Journal of Geophysical Research: Atmospheres
RESEARCH ARTICLE
10.1002/2013JD020707
Key Points:
• AgI cloud seeding impact on overshooting deep convection is simulated
• Cloud seeding could potentially modify stratospheric water vapor content
• Increase or decrease in LS water vapor
depends on seeding agent amounts
Correspondence to:
B. Chen,
[email protected]
Citation:
Chen, B., and Y. Yin (2014), Can we
modify stratospheric water vapor by
deliberate cloud seeding?, J. Geophys.
Res. Atmos., 119, 1406–1418,
doi:10.1002/2013JD020707.
Received 8 AUG 2013
Accepted 8 JAN 2014
Accepted article online 13 JAN 2014
Published online 3 FEB 2014
Can we modify stratospheric water vapor
by deliberate cloud seeding?
Baojun Chen1 and Yan Yin2,3
1
Key Laboratory of Mesoscale Severe Weather/MOE, and School of Atmospheric Sciences, Nanjing University, Nanjing,
China, 2Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of
Information Science and Technology, Nanjing, China, 3Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China
Abstract Stratospheric water vapor has an important effect on Earth’s climate. Considering the significance
of overshooting deep convection in modulating the water vapor content (WVC) of the lower stratosphere (LS),
we use a three-dimensional convective cloud model to simulate the effects of various silver iodide (AgI) seeding
scenarios on tropical overshooting deep convection that occurred on 30 November 2005 in Darwin, Australia.
The primary motivation for this study is to investigate whether the WVC in the LS can be artificially modified by
deliberate cloud seeding. It is found that AgI seeding done at the early stages of clouds produces significant
effects on cloud microphysical and dynamical properties, and that further affects the WVC in the LS, while
seeding at the mature stages of clouds has only a slight impact. The response of stratospheric water vapor to
changes in the amount of seeding agent is nonlinear. The seeding with a small (large) amount of AgI increases
(decreases) the WVC in the LS, due to enhanced (reduced) production and vertical transport of cloud ice from
the troposphere and subsequent sublimation in the stratosphere. The results show that stratospheric water
vapor can be artificially altered by deliberate cloud seeding with proper amount of seeding agent. This study
also shows an important role of graupel in regulating cloud microphysics and dynamics and in modifying the
WVC in the LS.
1. Introduction
Stratospheric water vapor has an important effect on Earth’s climate, and the changes in its concentrations
can have a significant impact on the trend of global warming [e.g., Solomon et al., 2010; Dessler et al., 2013].
The water vapor content of the stratosphere is controlled mainly by transport through the tropopause and
the oxidation of stratospheric methane [Ravishankara, 2012]. The overshooting deep convection as an important mechanism for cross-tropopause transport has attracted researchers’ considerable attentions, and
significant efforts have been put into both measurements [e.g., Corti et al., 2008; de Reus et al., 2009; Iwasaki
et al., 2012] and numerical simulations [e.g., Wang, 2003; Grosvenor et al., 2007; Chemel et al., 2009; Liu et al.,
2010] to investigate the effects of overshooting convection on the stratospheric water vapor.
Overshooting deep convection can potentially influence the water vapor content entering the stratosphere
by injecting ice particles above the tropopause which later evaporate (condensate) and thereby hydrate
(dehydrate) the stratosphere. Since the convective vertical transport is the main mechanism for ice particles
entering the stratosphere [Corti et al., 2008; Wang et al., 2011; Chen and Yin, 2011, hereinafter referred to as
CY11], the amount of ice particles injected into the stratosphere is associated with the ice particle number
concentration in the troposphere as well as the strength of convective storms. This has been verified recently
by modeling studies of Liu et al. [2010] and Grosvenor [2010] who found that strong overshoots transported
more ice mass and water vapor to the stratosphere relative to weak ones. Accordingly, the physical mechanisms that can stimulate changes in the number concentration of ice particles and the updraft strength for
overshooting deep convection have a potential influence on stratospheric water vapor content.
Cloud macrophysical and microphysical properties and precipitation processes of deep convective clouds are
affected by atmospheric aerosols which may act as cloud condensation nuclei (CCN) or ice nuclei (IN). This
modification of clouds and precipitation by environmental aerosols is also referred to as the inadvertent
aerosol seeding of clouds [Levin and Cotton, 2009]. A substantial review of aerosol impacts on convective
clouds and precipitation has recently been given by Tao et al. [2012]. It is shown that CCN or IN may enhance
or reduce the updraft strength and precipitation amount depending on the aerosol concentration, as well as
CHEN AND YIN
©2014. American Geophysical Union. All Rights Reserved.
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Journal of Geophysical Research: Atmospheres
10.1002/2013JD020707
environmental atmospheric conditions [e.g., Connolly et al., 2006; Ekman et al., 2007; Li et al., 2008; Fan et al.,
2009]. Recently, the CCN effects on overshooting deep convection have also been studied. Grosvenor et al.
[2007] and CY11 found that the dynamical and microphysical characteristics of tropical overshooting deep
convective clouds are affected by changes in CCN concentrations. Their results showed that increases in CCN
concentrations can lead to more cloud ice crystals transported to the lower stratosphere (LS), which eventually causes the LS to be moistened as these ice crystals evaporate. And the CCN-induced change in cloud
microphysics is found to play a particularly important role in regulating the water vapor content of the LS.
Compared to CCN, the effects of IN on stratospheric water vapor have not yet been addressed. Recent
modeling studies [e.g., Fan et al., 2010; Zeng et al., 2013] have shown that IN has a significant impact on the
anvil properties (e.g., areas, ice number, and mass) of tropical deep convective clouds and in turn further
affects water vapor content in the tropopause region [Fan et al., 2010]. However, it is not clear whether IN can
affect the water vapor content of the stratosphere.
Silver iodide (AgI) is a special type of IN. Since its effectiveness as a nucleus for ice crystal formation, AgI plays
a particularly important role in weather modification. Convective cloud seeding with AgI has been carried out
worldwide as a technique for enhancing precipitation or mitigating hail fall over the last several decades
[NRC, 2003; Qiu and Cressey, 2008]. In contrast to the inadvertent cloud seeding by background atmospheric
aerosols, the artificial seeding with AgI is a deliberate modification of clouds and precipitation by additionally
injecting appropriate amount of AgI particles into a supercooled cloud in specific time and location, for the
purpose of enhancing the formation of ice crystals and stimulating precipitation by ice particle growth so that
the cloud seeding can be artificially controlled. Previous studies have suggested that AgI seeding had a large
effect on the microphysical and dynamical properties of convective clouds and found that cloud microphysical changes induced by seeding played a very important role in determining storm development and
precipitation [e.g., Orville and Chen, 1982; Rosenfeld and Woodley, 1993; Farley et al., 2004; Ćurić et al., 2007,
2008; Chen and Xiao, 2010; Xue et al., 2013]. Modeling results of Farley et al. [2004] and Chen and Xiao [2010]
also showed an evident increase in the ice content in the upper tropospheric regions of convective clouds
due to seeding. This suggests that there is a possible link between AgI seeding of deep convective clouds and
stratospheric water vapor.
This paper extends the work of CY11 to explore the impact of AgI seeding on tropical deep convective clouds.
The fundamental difference is that in the CY11 study, the inadvertent effects of background aerosols were
examined, whereas here the primary goal is to investigate whether the water vapor content in the LS can be
artificially modified by deliberate cloud seeding. We address this issue by performing a series of sensitivity
experiments using the same cloud model and storm case in CY11. The next section briefly describes the
model and experimental design. Results from the sensitivity experiments are discussed in sections 3 and 4.
Finally, the summary and conclusions are presented in section 5.
2. Model and Experimental Design
2.1. Model Description
The model being used in this study is a three-dimensional, nonhydrostatic, and fully compressible convective
cloud model, developed by Kong et al. [1990] based on the dynamic framework of Klemp and Wilhelmson [1978].
The original microphysics parameterization adopted in the model is a one-moment bulk scheme that predicts
only the mixing ratio of the hydrometeor species. A two-moment bulk scheme with AgI seeding processes developed by Hong and Fan [1999] is subsequently implemented into the model to study hail formation mechanisms and seeding effects in hailstorms. The explicit droplet nucleation (CCN activation) was recently included in
the model by CY11 based on Rogers and Yau [1989] and Morrison et al. [2005] to investigate CCN impacts on
overshooting convection and stratospheric water vapor. The modified version of CY11 was used in the present
study to perform the simulation. In the current version, the model predicts the evolution of mass mixing ratios as
well as the number densities of the seven hydrometeor types cloud droplets, raindrops, cloud ice, snow, graupel,
frozen drops, and hail. The mixing ratio of AgI seeding agent is also predicted explicitly. For a detailed description
of model dynamics and microphysics, please refer to Hong and Fan [1999], or Chen and Xiao [2010] and Chen and
Yin [2011]. Only the key processes pertinent to this work are described here.
Ice crystals are formed by three microphysical processes: primary nucleation due to deposition and condensation freezing occurring on active natural ice nuclei, secondary ice production or ice multiplication, and
CHEN AND YIN
©2014. American Geophysical Union. All Rights Reserved.
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Journal of Geophysical Research: Atmospheres
10.1002/2013JD020707
homogeneous freezing of cloud droplets.
For primary ice nucleation, the number of
ice crystals initiated from natural ice nuclei
is specified as a function of temperature
according to Cooper [1986] and given by:
Ni ¼ 0:005 exp ½0:304ðT 0 T Þ;
(1)
where T0 = 273.15 K, T is the ambient air temperature (K), and Ni is the number of ice
crystals initiated (L1). For the current study,
cloud ice is allowed to initiate by equation
1 only when the water vapor mixing ratio exceeds 25% supersaturation with respect to
ice or water saturated and T < 260 K, following Thompson et al. [2008]. Ice multiplication
is specified to be the Hallett-Mossop riming/
splintering mechanism occurring at temperatures between 3°C and 8°C and is
parameterized following Hu and He [1988].
Figure 1. Skew-T log-p diagram plotted from the sounding taken in Darwin at
Homogeneous freezing of cloud droplets is
1430 local time, 30 November 2005. Wind vectors are shown on the right borassumed to occur instantaneously at temder (a full barb = 10 kt and a half barb = 5 kt). Adapted from Chen and Yin [2011].
peratures lower than 40°C. Once cloud
ice forms, it grows by vapor deposition
and riming processes. If cloud ice falls into warm (T > 0°C) air, it melts instantly to cloud water.
Snow is formed by depositional growth and riming of ice crystals and also by the collision and aggregation of
ice crystals. Snow continues to grow by deposition or collection of cloud ice and cloud water. Cloud ice and
snow can be converted to graupel by riming. The autoconversion rate coefficient for cloud ice to snow and
snow to graupel is based on Lin et al. [1983]. Graupel may be also generated by probabilistic freezing of raindrops and collisional freezing of rain with cloud ice, snow, or AgI particles when the raindrop diameter is smaller
than 1 mm. The freezing of large raindrops (diameter more than 1 mm) is converted to frozen drops. The density
of frozen drops is set to a fixed value of 0.9 g cm3. While the density of graupel varies with the graupel content
and is assumed to be 0.12 g cm3 if the graupel content is less than 0.5 g m3; otherwise, it is assumed to be
0.4 g cm3. Once generated, graupel and frozen drops may grow by collection of cloud water, rain, cloud ice,
and snow, or by vapor deposition. Graupel and frozen drops are converted to hail when their diameters are
greater or equal to 5 mm. It has been shown by Kovačević and Ćurić [2013] that the microphysics scheme
distinguishing graupel and frozen drops is better for simulation of precipitation in convective clouds.
The scheme employed here to initialize ice formation by AgI is mainly based on Hsie et al. [1980]. Following this
scheme, the AgI particles are assumed to be monodisperse with a radius of 0.1 μm and a mass of 2.38 × 1017 kg.
The terminal velocity of AgI particles is neglected. The nucleation mechanisms by which AgI can produce ice
crystals include condensation-freezing/deposition nucleation and contact freezing nucleation. Contact
nucleation mechanisms are limited to inertial impact and Brownian collection. Only one active AgI particle
can be captured by one liquid drop for contact freezing nucleation. But impact scavenging of AgI particles
by raindrops is not considered here. The graupel and frozen drops can be also formed by contact freezing of
raindrops with AgI particles. The relevant seeding parameterizations have been described in detail by Hong
and Fan [1999] and Chen and Xiao [2010], which will not be repeated in this work. The nucleating efficiency
of AgI is based on a fitting temperature-dependent activation curve [Hsie et al., 1980]. Activation only occurs
in the cloudy environment and when the temperature is below 5°C. If the temperature is further colder
than 20°C, all of the AgI particles are activated, and the maximum number concentration of active nuclei is
160 L1 approximately. For the temperature between 5 and 20°C, the number of active nuclei, Na (L1), is
prescribed as a function of temperature and given by [Hsie et al., 1980]:
h
i
Na ¼ exp 0:022ðT 0 T Þ2 þ 0:88ðT 0 T Þ 3:8 ;
(2)
where T is the ambient air temperature (K), and T0 = 273.15 K.
CHEN AND YIN
©2014. American Geophysical Union. All Rights Reserved.
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Journal of Geophysical Research: Atmospheres
10.1002/2013JD020707
Table 1. Some Features for Cases t20z60a
1
1
1
Cases
XS0 (g g )
[AgI] (g)
TMASS (KT)
TP (KT)
Ipart (%)
IWMLS (%)
WVMLS (%)
WTROP (m s )
WLS (m s )
unseeded
1
2
3
4
5
6
7
8
9
10
11
12
/
13
2.0 × 10
12
1.0 × 10
12
3.0 × 10
12
8.0 × 10
11
1.7 × 10
11
3.0 × 10
11
4.5 × 10
11
8.0 × 10
10
1.5 × 10
10
3.0 × 10
10
5.0 × 10
9
1.0 × 10
/
12
61
184
490
1042
1839
2758
4904
9195
18,390
30,650
61,300
54,515.0
54,749.3
55,186.9
55,677.5
56,448.8
57,672.5
58,500.4
59,537.1
60,511.3
61,650.9
62,462.5
62,882.8
63,297.3
39,824.4
40,005.7
40,552.6
41,153.5
41,969.2
43,099.7
43,967.8
45,137.8
46,257.1
47,605.7
48,639.2
49,026.8
49,344.7
50.7
51.2
51.8
52.1
52.7
54.1
54.8
55.8
56.7
58.0
58.9
59.5
60.4
/
+16.6
+26.9
+29.8
+27.5
+10.9
+8.37
3.85
9.52
14.4
16.0
8.96
7.11
/
+0.092
+0.150
+0.172
+0.148
+0.039
+0.005
0.053
0.091
0.120
0.136
0.114
0.102
43.1
43.3
43.2
43.0
42.5
42.0
41.9
41.5
41.3
41.2
41.0
40.2
40.4
16.8
16.3
15.5
15.2
15.3
15.7
15.6
15.5
15.3
14.8
14.1
14.3
14.4
a
XS0 is maximum value of the initial AgI mixing ratio; [AgI] is total amount of seeding agent injected in the domain; TMASS and
TP are accumulated total mass of condensate and precipitating hydrometeors (rain, snow, graupel, frozen drops, and hail),
obtained from the integration over the entire domain and duration time, respectively; Ipart is ice-phase hydrometeor (cloud
ice, snow, graupel, frozen drops, and hail) mass partitioning in the storms; IWMLS and WVMLS are seeding-induced changes in
cloud ice and water vapor mass in the LS expressed in percentage, respectively. Maximum updraft velocity in the storm and LS
is represented by WTROP and WLS, respectively.
2.2. Experimental Setup
The overshooting deep convection that developed on 30 November 2005 in Darwin, Australia, during the
Stratospheric-Climate Links with Emphasis on the Upper Troposphere and Lower Stratosphere/Aerosol and
Chemical Transport in Tropical Convection (SCOUT-O3/ACTIVE) campaign was simulated in the present study.
Figure 1 shows the profiles of temperature and dew-point temperature taken from Darwin, on 30 November
2005 at 1430 LT, which presents a very large convective available potential energy of approximately 3500 J kg1.
The wind hodograph shows a weak vertical wind shear and a small horizontal wind speed of less than 5 m s1 in
the troposphere. We have examined the impacts of initial CCN concentrations on the simulated storm characteristics and stratospheric water vapor content in CY11. Here the model setups were similar to those of CY11. All
of the simulations were integrated to 4200 s using a horizontal grid spacing of 0.5 km over a 41 km × 41 km
domain and a vertical grid spacing of 0.3 km over a 24 km depth. For a given spatial resolution, the model uses
the large time step of 2 s and the small one of 0.25 s, respectively. Convection was initiated by a warm bubble
of 20 km wide and 3 km deep, placed at 1.5 km above ground level and at the horizontal center of the model
domain. The maximum thermal perturbation was 2 K in the center of the bubble, with the amplitude decreasing
according to a cos2 relationship with radial distance from the center. Sensitivity tests showed that such heating
overall produced a better simulation as compared to the radar observations [refer to CY11]. Considering that the
cold point tropopause was situated at 17.3 km altitude for the current event [de Reus et al., 2009] and the vertical
grid size used in the simulations, the LS boundary herein is defined at 17.4 km.
The initial distribution of the seeding agent, similar to Hsie et al. [1980] but extended vertically and horizontally over three grid points, which forms a cubic block, has maximum values in the center and decreases
with distance outward. The seeding agents were released continuously for 300 s, and the height of the center
of the initial seeding agent is located at 6.0 km level (5°C approximately). Two groups of seeding experiments have been carried out to investigate the effects of various seeding scenarios. The first one (denoted by
t20z60) was seeded in the region of the strongest updraft when the model cloud top was passing the 10°C
level at 20 min (cloud-growing stage). The other one (denoted by t30z60) is designed to test the effects of
time of the seeding agent released by seeding at a later time, that is, 10 min later than in t20z60 when the
simulated storm is at the mature stage. Considering that the number concentrations of ice particles nucleated newly by seeding are closely related to the seeding agent amount, for each seeding group, we design
12 sensitivity runs with the core values of the AgI mixing ratio ranging from 2 × 1013 g g1 to 1 × 109 g g1
to survey the response of the LS water vapor content to the changes in the amount of AgI seeding agent
(hereinafter referred to as [AgI]). Specific values used in the present simulations are listed in Table 1. Cloud
features of sensitivity experiments from cases t20z60 are also shown in the table as this seeding group
obtained a more significant effect relative to the other group. Since the primary motivation for this study is to
address the possibility of artificially modifying stratospheric water vapor, the background aerosol impacts on
CHEN AND YIN
©2014. American Geophysical Union. All Rights Reserved.
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Journal of Geophysical Research: Atmospheres
10.1002/2013JD020707
the seeding effects are not considered. All of the
numerical experiments are assumed to perform
at the maritime CCN scenarios. In other words,
the current research focuses on maritime convective storms.
3. Does Seeding Affect LS Water
Vapor Content?
Figure 2 shows the seeding-induced percentage
change in the accumulated total mass of water
vapor in the LS that is the overall amount produced throughout the integration. A positive
(negative) value means an excess (a loss) for the
seeded case relative to the unseeded case.
Comparing the two seeding groups, the change
in water vapor mass for cases t30z60 (dashed
line) is much smaller, even under the condition
Figure 2. Changes in accumulated total water vapor mass in the lower
stratosphere as a function of the total silver iodide (AgI) amount injected
of extremely high [AgI] (maximum magnitude
into the domain, relative to the unseeded case. Solid and dashed lines reponly 0.01% approximately). Whereas a large
resent the t20z60 and t30z60 cases, respectively. A positive value means an
variation is found for cases t20z60 (solid line),
excess for the seeded case.
ranging from 0.136% to +0.172% (Table 1). The
results show that the AgI seeding does not
modify the water vapor content of the LS significantly when the seeding was carried out at the mature stage of
convective storms. When the seeding was carried out at the early cloud-growing stage, however, the seeding
has a large influence on the LS water vapor. With the mean over the stratospheric domain and over the whole
70 min integration, the seeding-induced increase or decrease in water vapor mixing ratio reaches a maximum
of 1.463 ppbv and 1.114 ppbv, respectively. Compared with the background water vapor mixing ratio of
1.435 ppmv derived from the initial sounding, the seeding-induced change is 1‰ approximately.
At the end of the simulation, the total mass of water vapor in the LS for seeded cases is increased by ~4.3–
5.7% compared to the simulation start. For unseeded case, the water vapor mass is found to be increased by
5% [CY11]. Hence, based on the comparison between the unseeded and seeded cases, it is concluded that
seeding leads to an extra moistening of the LS up to ±0.7%.
Given the significance of ice particles of the LS in
determining the stratospheric water vapor content
[e.g., Corti et al., 2008; Chemel et al., 2009; CY11], it
is necessary to examine the seeding effects on
stratospheric ice particles. Figure 3 presents the
changes induced by seeding in the total mass of
ice hydrometeors (cloud ice, snow, graupel, frozen
drops, and hail) in the LS. One can see that the LS
ice mass is affected significantly for cases t20z60,
showing a 15.5% to +30.1% change. Compared
to t20z60, the change in ice mass is insignificant
for cases t30z60. The results show that seeding at
the early stages of storms can modify the stratospheric ice mass significantly.
Figure 3. As Figure 2 but for total ice mass.
CHEN AND YIN
As shown in Figures 2 and 3, both the ice and
water vapor mass in cases t30z60 decrease
slightly due to seeding, and the magnitude of
reduction increases with increasing [AgI]. From
the results of cases t20z60, it is found that both
the ice and water vapor mass in the LS change
©2014. American Geophysical Union. All Rights Reserved.
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Journal of Geophysical Research: Atmospheres
Figure 4. The amount of AgI seeding agent in the model domain as
a function of time for cases t20z60e3k (solid line) and t20z60e30
(dashed line).
10.1002/2013JD020707
nonlinearly with [AgI]: increase in the low [AgI] runs
but decrease in the high [AgI] runs, indicating that
the seeding with a small (large) amount of AgI
seeding agent leads to more (less) ice and water
vapor in the LS. This critical seeding amount is
~2000 g in this case. The results suggest that the
response of the water content in the LS to changes
in the seeding agent amount is nonlinear, and this
is likely due to the complicated interaction between cloud microphysics and dynamics. Detailed
analysis on the seeding effects will be presented in
the following sections. Note that the accumulated
total ice mass and total water vapor mass in seeded
cases shown in Figures 2 and 3 exhibit similar
changes; this is because sublimation of injected ice
particles from troposphere is an important contributor to stratospheric water vapor [e.g., CY11].
The figures also suggest that the stratospheric
water vapor would be potentially increased or
decreased further given more time.
4. Mechanisms Affecting LS Water Vapor
4.1. Sinks and Conservation of Seeding Agent
In this section, a representative pair of experiments from the group of t20z60, denoted t20z60e30 and
t20z60e3k, which obtained the greatest increase (+0.172%) and decrease (0.136%) in the LS total water
vapor mass, respectively, were selected to analyze the seeding effects. These two cases are run with the same
initial conditions except that t20z60e3k is seeded with a larger amount of seeding agent. At the time of initial
seeding (at 20 min), the simulated cloud top has already reached the 7.2 km altitude (13.4°C level), and the
updraft core is located at 4.5 km with a maximum value of 23.4 m s1. With being released in the seeding
region continuously, the amount of AgI seeding agent in the domain increased gradually and reached its
peak at the end of seeding (at 25 min) for both the two cases (Figure 4). Approximately a total of 184 and
18,390 g of AgI were injected for t20z60e30 and t20z60e3k, respectively. Most of the seeding agent has been
nucleated within 5 min after seeding (25–30 min), accounting for over 80% of the total consumption. At the
end of simulation, the total consumption of seeding agent is 162 and 16,313 g in t20z60e30 and t20z60e3k,
respectively, accounting for over 88% of the seeding amounts. This indicates that only 8% of AgI is decreased
due to strong advection through the boundaries, because of less than 4% of seeding agent remaining in the
domain by the end of the simulation. Overall, conservation of the seeding agent mass is good. Deposition
nucleation or condensation-freezing nucleation is the most important mechanism responsible for cloud ice
production by seeding, accounting for 97.5% of the total sink of the seeding agent. Contact freezing of
raindrops, mainly through the Brownian motion mechanism, is the second largest AgI sink, with a 2% contribution. Other nucleation modes contribute less than 0.5%.
4.2. Dynamical and Microphysical Effects of Seeding on Storms
The effects of seeding on storm dynamics are first examined. For the current event, the upward transporting
ice particles across the tropopause are found to occur during the time period 30–34 min and 38–50 min,
which correspond to the strongest stage of the storm life cycle and the local stratospheric convection
developing stage, respectively (see CY11). The cross-tropopause transport of ice particles into the LS at the
first stage is largely determined by the storm main updraft, while the LS updraft dominates the second stage.
The maximum storm main updraft velocity is 43.1 m s1 occurring at 32 min and 13.2 km altitude in the
unseeded case. For case t20z60e30, the maximum main updraft velocity is 43.0 m s1, occurring at 31 min
and 12.9 km altitude. One can see that the peak updraft velocity in t20z60e30 is almost the same as in the
unseeded case. This suggests that for the light seeding case, seeding does not induce much dynamic change.
For case t20z60e3k, the maximum updraft velocity is 41.0 m s1, occurring at 31 min and 12.6 km altitude.
CHEN AND YIN
©2014. American Geophysical Union. All Rights Reserved.
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Journal of Geophysical Research: Atmospheres
10.1002/2013JD020707
Compared to the unseeded case, case
t20z60e3k has a weaker updraft and a
lower updraft core. Seeding also affects
updraft intensity in the stratosphere. It is
found that a weaker cold trap region
near the tropopause is formed in the
seeded case, thus resulting in a weaker
descent above the cloud top and a
weaker convection in the LS, with the
maximum updraft of 15.2 m s1 for
t20z60e30 and 14.1 m s1 for t20z60e3k,
respectively, as compared to the
unseeded case, which has a stratospheric updraft maxima of 16.8 m s1.
These results indicate that AgI seeding
weakens the intensity of tropical
overshooting convection.
Changes in updraft velocities can further
affect the upward transport of air mass.
Figure 5 shows the change in updraft
mass flux due to seeding at a given time
Figure 5. Time vs height section of the differences between the seeded (t20z60e30
and altitude, which is computed from,
in Figure 5a, and t20z60e3k in Figure 5b) and unseeded cases in areally integrated
6
1
and ρa is air density, w is updraft magupdraft mass flux (10 kg s ). Dashed line indicates the 17.4 km altitude, here
defined as the lower boundary of the stratosphere.
nitude, and A is area. One can see that
tropospheric updraft mass flux in both
seeded cases is increased before 30 min, i.e., within 5 min after seeding, and is decreased thereafter. The
increase or decrease in case t20z60e3k is even larger. Significant changes in updraft mass flux crossing the
tropopause can be found to occur after 30 min when the simulated storm evolves into the mature stage.
Roughly, the early flux is increased, and the later is decreased in both cases. The net flux is still increased in
t20z60e30 and is decreased in t20z60e3k, suggesting that there would be more (less) transporting upward
into the LS in t20z60e30 (t20z60e3k).
Figure 6. Comparison of the temporal evolution of domain-averaged number concentration of the ice crystals in the troposphere (below 17.4 km in this work) for
the unseeded (green) and two seeded cases: t20z60e30 (blue) and t20z60e3k (red).
CHEN AND YIN
©2014. American Geophysical Union. All Rights Reserved.
During the upward transporting ice particles across the tropopause, the number
of ice crystals in the troposphere is key to
determining the ice mass in the LS. In our
simulations, ice crystals dominate the LS
accounting for ~99.98% of the total
hydrometeor number concentration. By
comparison, the other ice hydrometeors
(snow, graupel, frozen drops, and hail)
contribute less than 0.02% of the total,
and hence their contributions to the
change in stratospheric water vapor
content are negligible when compared
to the ice crystals. Figure 6 shows the
temporal evolution of domain-averaged
number concentration of ice crystals in
the troposphere for the unseeded and
two seeded cases. The tropospheric
region herein is defined below 17.4 km
referred to the tropopause height and
the vertical grid size used in the simulations. One can see that the difference in
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Journal of Geophysical Research: Atmospheres
10.1002/2013JD020707
ice crystal concentrations between seeded and
unseeded cases is small in the early seeding stage.
After 30 min, however, the ice crystal concentration
evolution shows a large difference in the two
seeded cases. Case t20z60e30 has more ice crystals
in the troposphere than the unseeded case; in contrast, the ice crystal concentration is significantly
lower in t20z60e3k than that in the unseeded case.
Overall, the ice crystal concentration has been increased by 9.6% in t20z60e30 but decreased by
37.6% in t20z60e3k, showing that more (fewer) ice
crystals are produced in light (heavy) seeding case.
Previous study by CY11 has shown that ice crystals
in the stratosphere are transported upward by
overshooting convection from the troposphere.
The overshooting activity in the simulated case
begins at 30 min. Figure 7 shows the time evolution
Figure 7. Time evolution of the domain-averaged cloud ice mixing
3
1
of the cloud ice mixing ratio averaged over the LS
ratio (10 g kg ) in the lower stratosphere for the unseeded (green)
and two seeded cases: t20z60e30 (blue) and t20z60e3k (red).
domain for seeded and unseeded cases. One can
see that cloud ice occurs in the LS after 30 min, and
the content exhibits a similar trend for the three cases. Compared to the unseeded case, during the period
covering the overshooting, more (less) cloud ice entered the stratosphere due to enhanced (reduced) vertical
transport and increased (decreased) tropospheric ice crystal concentrations for t20z60e30 (t20z60e3k).
How does seeding affect cloud ice production? The differences between the seeded and unseeded cases for
various production terms for cloud ice concentrations versus time are shown in Figure 8. Differences in rime
splintering multiplication are insignificant and are not included. Comparing Figures 6 and 8, the initial increase in
cloud ice concentrations in t20z60e3k is clearly due to the seeding by which water vapor is converted to cloud ice
(NUxai). The later decrease in cloud ice concentrations is due to the decrease in homogeneous freezing of droplets
(HFZci) and deposition nucleation on natural ice nuclei (NUvi). In the model, natural deposition nucleation is based
on a temperature-dependent scheme given in
equation 1. Nevertheless, changes in water vapor
content may have an impact on deposition nucleation because it is limited to occur only when the air
is saturated with respect to water and T < 260 K or
the water vapor mixing ratio exceeds 25% supersaturation with respect to ice. Seeding decreases
the water vapor content, thereby resulting in the
reduction in cloud ice production through natural
deposition nucleation. Compared with the difference between t20z60e3k and unseeded case in
NUxai and NUvi, the seeding-induced change in
the two production terms is very small for case
t20z60e30 (solid lines in Figure 8).
Figure 8. The differences in the production terms for cloud ice concentrations as a function of time between the seeded and unseeded cases.
Solid and dashed lines represent the case t20z60e30 and t20z60e3k,
respectively. NUvi and NUxai denote the primary nucleation of cloud ice
occurring on natural ice nuclei and AgI particles, respectively. HFZci is
homogeneous freezing of cloud droplets. A positive value means an
excess for the seeded case.
CHEN AND YIN
The seeding influences homogeneous freezing of
droplets to form ice crystals significantly. In our
simulations, the main sinks for cloud water are
accretion by rain, graupel, frozen drops, and hail
and homogeneous freezing. Collection of rain is
the largest sink for cloud water and dominates the
cloud water content. Seeding reduces the collection of cloud water by rain during 20–30 min
because of the enhanced depletion of the rain
water through the accretion by the early seeding-
©2014. American Geophysical Union. All Rights Reserved.
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Journal of Geophysical Research: Atmospheres
Figure 9. Domain-averaged number concentration of (a) graupel and
(b) frozen drops in the troposphere for the unseeded case (green) and
the seeded cases of t20z60e30 (blue) and t20z60e3k (red).
10.1002/2013JD020707
increased ice crystals and AgI particles. Despite the
earlier formation and increased amounts of graupel
and frozen drops in seeded cases (Figure 9) enhance the depletion of the cloud water due to the
riming process at the same time, the cloud water is
still greater in the seeded case than in the unseeded
case, because the decreased depletion of the cloud
water by the rain collection is much greater than the
increased depletion by the riming process on graupel or frozen drops. With the enhanced transporting
upward at earlier times in seeded cases (Figure 5),
more cloud water can ascend to altitudes where the
homogeneous freezing is initiated (the fall velocity
of droplet is neglected in this study), resulting in the
increase in homogeneous freezing of cloud droplets. Note that this increase is more significant in
t20z60e30 than that in t20z60e3k. This is because
the seeding increases only slightly the concentration of graupel and frozen drops for t20z60e30 and
thus depletes less cloud water compared with
t20z60e3k. As simulated storms evolve into the
mature stage, the riming of graupel and frozen
drops is the most important depletion terms of
supercooled cloud water. As is indicated in Figure 9,
the seeding causes substantial increases in graupel
and frozen drops after 30 min for t20z60e3k, as a
result, less cloud water available for the homogeneous freezing which is decreased significantly in
the seeded case. Compared with t20z60e3k, the
decrease in the homogeneous freezing is small for
t20z60e30, since the seeding has only slightly
increased the concentration of graupel and frozen
drops. Overall, for t20z60e30 (t20z60e3k), the
increased amounts of ice crystals at the earlier
time through the homogeneous freezing of cloud
droplets are markedly greater (smaller) than the
decreased amount at the later time, thus leading to
more (less) cloud ice produced in the seeded case
as compared to the unseeded case.
Both graupel and frozen drops are increased by seeding (Figures 9a and 9b). Nevertheless, frozen drops are
found to play a small role in seeding effects because their concentrations are substantially low. On average,
the number concentration of frozen drops is two orders of magnitude lower than that of graupel approximately. This is due to the fact that frozen drops are generated only by the freezing of large raindrops, while
graupel can be formed by the freezing of small raindrops, or by aggregation of ice crystals or snow. Note that
the differences between t20z60e3k and unseeded case for graupel concentrations are much pronounced,
showing a 505% increase in the total compared to the unseeded case. In contrast, t20z60e30 does not produce significant differences compared to t20z60e3k (the total concentration of graupel increases only 3.4%).
The main mechanisms by which seeding causes increases in graupel production are the collisional freezing of
raindrops with AgI and seeding-increased ice crystals or snowflakes. Their production rates are directly
related to the number of AgI particles and increase with increasing seeding agent amounts. The insignificant
differences between t20z60e30 and unseeded case are due to the reason that the amount of AgI used in the
experiment is much less.
The seeding results in substantial increases in graupel particles for t20z60e3k. These large amounts of graupel
particles grow by riming and deposition enhancing depletion of droplets and water vapor, leading to the
CHEN AND YIN
©2014. American Geophysical Union. All Rights Reserved.
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Journal of Geophysical Research: Atmospheres
Figure 10. Accumulative total mass of the hydrometeor for the unseeded
(green) and the seeded cases of t20z60e30 (blue) and t20z60e3k (red). RA,
SN, GP, FZ, and HA represent the rain, snow, graupel, frozen drops, and
hail, respectively. TP is the total precipitating hydrometeor. Numbers at
the right side of columns are the percentage increase or decrease compared to the unseeded cases.
10.1002/2013JD020707
formation of less cloud ice at the later times. The
question arises: can the high concentrations of
graupel particles be transported upward to the
stratosphere or not? This question is also important because sublimation/deposition of the
injected graupel particles can provide an additional source/sink of water vapor. There are, in
fact, many fewer graupel particles to enter the
stratosphere crossing the tropopause in the
seeded case than in the unseeded case since
seeding reduced vertical transport, although
more graupel was produced in the seeded case.
Domain-averaged accumulative number concentrations of graupel particles for the stratosphere
are found to be only 0.4 and 1.1 m3 in t20z60e3k
and the unseeded case, respectively. Those
values are significantly lower as compared to the
number concentration of ice crystals, which are
308.7 and 136.0 L1 for the unseeded case and
t20z60e3k, respectively. This indicates a negligible impact of seeding-induced change in upward
transport of graupel particles through the tropopause on the water vapor content in the LS.
Compared to the unseeded case, the seeded case
has more precipitation mass. Accumulated total mass of precipitating hydrometeors (the total of rain, snow,
graupel, frozen drops, and hail) is 41,153 KT for t20z60e30 and 48,639 KT for t20z60e3k; both values are
greater than the 39,824 KT for the unseeded case. The total precipitation mass is increased by 3% and 22%,
respectively. In a shearing ambient wind field, the seeding-increased precipitation mass can enhance storm
growth through enhanced downdrafts [Simpson, 1980]. This case, however, is characterized by weak wind
shear in the low troposphere. Therefore, the
loading effect of the increased precipitation mass
in the seeded case suppresses the upward motion
and causes the storm to have a weaker updraft
as compared to the unseeded case. Despite the
release of latent heat caused by AgI nucleation
stimulates the storm updraft, this only occurs before 30 min, i.e., within 5 min after seeding. When
the simulated storms have reached mature
stages, the loading effect of precipitation mass
dominates the storm vertical growth. Figure 10
shows clearly that graupel is the most main contributor for seeding-increased precipitation mass,
accounting for 100% and 94% of the total increase in precipitation mass for t20z60e30 and
t20z60e3k, respectively. This suggests that the
seeding-increased graupel is mainly responsible
for suppressed updrafts in seeded cases. Analysis
mentioned above shows that graupel plays a key
role in the context of seeding effects since it in8
1
Figure 11. Domain-averaged production of cloud ice (unit in 10 g kg )
fluences both microphysical cloud ice formation
in the lower stratosphere due to deposition (solid line) and sublimaand vertical transport.
tion (dashed line) for the unseeded case (green) and the seeded cases
of t20z60e30 (blue) and t20z60e3k (red). Sublimation dominates the
ice microphysical processes in the LS, especially in the later stages of
the simulations.
CHEN AND YIN
Results from the representative two experiments
indicate that AgI seeding with a proper amount of
©2014. American Geophysical Union. All Rights Reserved.
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Journal of Geophysical Research: Atmospheres
Figure 12. Time evolution of the domain-averaged water vapor mixing
3
1
ratio (10 g kg ) in the lower stratosphere for the unseeded (green)
and two seeded cases: t20z60e30 (blue) and t20z60e3k (red).
10.1002/2013JD020707
seeding agent could influence the cloud microphysics and dynamics of tropical overshooting
deep convective storms and further cloud ice
content entering the LS. The injected ice crystals
can grow by deposition; however, sublimation
dominates all cases, especially in the later stages
(Figure 11). Compared with the unseeded case,
sublimation is enhanced (reduced) in the light
(heavy) seeding case due to increased (decreased) cloud ice concentrations; as a result,
the water vapor content in the LS is increased
(decreased) eventually (Figure 12). Note that the
difference between unseeded and seeded cases
in water vapor mixing ratio increases with time
evolution. Considering that rather cloud ice is still
left in the stratosphere at the end of simulation,
thus, the stratospheric water vapor would be
potentially altered further given more time.
5. Summary and Conclusions
This paper is an extension of previous work where we investigated the effects of atmospheric aerosols acting
as CCN on stratospheric water vapor through overshooting deep convection. In the present work, we simulated the effects of artificial seeding with AgI on cloud microphysics and dynamics of overshooting deep
convection. The primary goal is to examine whether the water vapor content in the lower stratosphere (LS)
can be artificially modified by deliberate cloud seeding.
The results show that seeding done at the early stages of clouds has a significant effect on water vapor
content in the LS and that either the water vapor may increase or decrease depending on seeding agent
amount. It is found that the water vapor content is increased in light seeding case, whereas it is decreased in
heavy seeding case, indicating that the response of the LS water vapor content to changes in the amount of
seeding agent is nonlinear. Seeding done at the mature stage of clouds does not affect the LS water vapor
content significantly.
Seeding affects on stratospheric water vapor with having dynamical and microphysical effects. In the absence of
vertical wind shear, the seeding-enhanced loading effect of precipitation mass mainly in the form of graupel
suppresses the upward motion and causes the storm to have weaker updrafts. The seeding-increased graupel
particles enhance depletion of supercooled cloud water, and that reduces the formation of ice crystals later due
to less homogenous freezing of droplets. The degree of reduction both in updrafts and cloud ice formation
increases with the seeding agent amount. The combination of microphysical and dynamical effects caused by
seeding makes more (less) upward transporting of ice crystals into the LS in the light (heavy) seeding scenarios,
thereby resulting in the increase (decrease) in water vapor content of the stratosphere. The results also indicate
that changes in graupel number and mass induced by seeding play a very important role in determining cloud
properties and water vapor content in the stratosphere.
The present study focused on one overshooting event only. There is, in fact, a high occurrence frequency of
the overshooting deep convection on a global scale, especially over land [Iwasaki et al., 2010]. Hence, seeding
could potentially modify stratospheric water vapor to a greater extent if more overshooting convections
are seeded.
It should be noted that the findings of this paper were limited to the AgI nucleation parameterizations proposed by Hsie et al. [1980]. Other nucleation parameterizations such as proposed by Demott [1995] and
Meyers et al. [1995] should be examined in future studies due to the difference in nucleation modes.
Moreover, the results of the present study were only based on a tropical thunderstorm under maritime
climatic conditions. Modeling study by Lin et al. [2005] has shown significant differences in microphysical
structures between thunderstorms in different climate regimes, such as the US High Plains vs maritime
subtropics. Whether the conclusions in this paper can be extrapolated to other regions remains to be
CHEN AND YIN
©2014. American Geophysical Union. All Rights Reserved.
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Journal of Geophysical Research: Atmospheres
10.1002/2013JD020707
confirmed, especially for midlatitude continental regions, in which overshooting deep convections also have
an important role in regulating the water vapor content of the lower stratosphere [Wang, 2003; Wang et al.,
2011]. We leave this subject for future work.
Acknowledgments
This work was primarily supported by
the National Natural Science
Foundation of China (41175118 and
40775005). Research results presented
in this paper were jointly supported by
the National Basic Research Program of
China (2014CB441403 and
2013CB430105) and the China Special
Fund for Meteorological Research in the
Public Interest (GYHY201306040).
Baojun Chen was also supported by the
Jiangsu Collaborative Innovation Center
for Climate Change. Simulations were
performed on the IBM Blade cluster
system in the High Performance
Computing Center (HPCC) of Nanjing
University. The authors acknowledge
the SCOUT-O3/ACTIVE team for providing the sounding used in this research.
They also thank Dr. Xiping Zeng and
two anonymous reviewers for their critical yet constructive comments.
CHEN AND YIN
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